Improving the Measurement of Cognitive Ability in Geriatric Patients
Bibliographic record
Abstract
BACKGROUND/AIMS: No single tool is available in geriatric clinical settings to quantitatively measure global cognitive ability at different stages ranging from normal functioning to severe impairment. We developed an adaptive test to measure cognitive ability in geriatric populations (Geriatric Rapid Adaptive Cognitive Estimate, GRACE); however, the test failed to discriminate between high-functioning individuals. This study aimed to estimate the extent to which adding more difficult items to the GRACE would improve measurement precision in the upper range of cognitive ability. METHODS: The original data used to develop the GRACE was merged with newly collected data of patients who presented at local geriatric clinics. The Rasch analysis was used to estimate the difficulty level of the newly added items and evaluate whether the psychometric properties of the GRACE were improved. RESULTS: One newly added item (Sequencing 5) had a higher difficulty level than all of the previous items in the GRACE. The rest of the new items were located in the high difficulty range. CONCLUSION: The psychometric properties of our adaptive screening tool were improved, and we were able to distinguish between individuals who had higher levels of cognitive functioning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".